





'''simple docstring'''


import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
    level=os.environ.get('LOGLEVEL', 'INFO').upper(),
    stream=sys.stdout,
)

a__			:					int       						=						logging.getLogger(__name__)

a__			:					Dict       						=						{'facebook/bart-base': BartForConditionalGeneration}
a__			:					Optional[Any]       						=						{'facebook/bart-base': BartTokenizer}
def       __snake_case		( )							->     str:

       """simple docstring"""


       UpperCAmelCase  =						argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.''' )
       parser.add_argument(
           '''--validation_file'''			,	type=SCREAMING_SNAKE_CASE_			,	default=SCREAMING_SNAKE_CASE_			,	help='''A csv or a json file containing the validation data.''' )
       parser.add_argument(
           '''--max_length'''			,	type=SCREAMING_SNAKE_CASE_			,	default=5			,	help='''The maximum total input sequence length after tokenization.'''			,	)
       parser.add_argument(
           '''--num_beams'''			,	type=SCREAMING_SNAKE_CASE_			,	default=SCREAMING_SNAKE_CASE_			,	help=(
               '''Number of beams to use for evaluation. This argument will be '''
               '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
           )			,	)
       parser.add_argument(
           '''--model_name_or_path'''			,	type=SCREAMING_SNAKE_CASE_			,	help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,	required=SCREAMING_SNAKE_CASE_			,	)
       parser.add_argument(
           '''--config_name'''			,	type=SCREAMING_SNAKE_CASE_			,	default=SCREAMING_SNAKE_CASE_			,	help='''Pretrained config name or path if not the same as model_name'''			,	)
       parser.add_argument(
           '''--device'''			,	type=SCREAMING_SNAKE_CASE_			,	default='''cpu'''			,	help='''Device where the model will be run'''			,	)
       parser.add_argument('''--output_file_path'''			,	type=SCREAMING_SNAKE_CASE_			,	default=SCREAMING_SNAKE_CASE_			,	help='''Where to store the final ONNX file.''' )

       UpperCAmelCase  =						parser.parse_args()

       return args
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			List[str]="cpu" )							->     Optional[int]:

       """simple docstring"""


       UpperCAmelCase  =						model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE_ )

       if model_name in ["facebook/bart-base"]:
              UpperCAmelCase  =						0
              UpperCAmelCase  =						None
              UpperCAmelCase  =						0

       return huggingface_model, tokenizer
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Tuple			,	SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			List[Any] )							->     Union[str, Any]:

       """simple docstring"""


       model.eval()

       UpperCAmelCase  =						None
       UpperCAmelCase  =						torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE_ ) )

       with torch.no_grad():
              UpperCAmelCase  =						'''My friends are cool but they eat too many carbs.'''
              UpperCAmelCase  =						tokenizer([ARTICLE_TO_SUMMARIZE]			,	max_length=1_024			,	return_tensors='''pt''' ).to(model.device )

              UpperCAmelCase  =						model.generate(
                  inputs['''input_ids''']			,	attention_mask=inputs['''attention_mask''']			,	num_beams=SCREAMING_SNAKE_CASE_			,	max_length=SCREAMING_SNAKE_CASE_			,	early_stopping=SCREAMING_SNAKE_CASE_			,	decoder_start_token_id=model.config.decoder_start_token_id			,	)

              torch.onnx.export(
                  SCREAMING_SNAKE_CASE_			,	(
                      inputs['''input_ids'''],
                      inputs['''attention_mask'''],
                      num_beams,
                      max_length,
                      model.config.decoder_start_token_id,
                  )			,	SCREAMING_SNAKE_CASE_			,	opset_version=14			,	input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']			,	output_names=['''output_ids''']			,	dynamic_axes={
                      '''input_ids''': {0: '''batch''', 1: '''seq'''},
                      '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
                  }			,	example_outputs=SCREAMING_SNAKE_CASE_			,	)

              logger.info('''Model exported to {}'''.format(SCREAMING_SNAKE_CASE_ ) )

              UpperCAmelCase  =						remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE_ ) )

              logger.info('''Deduplicated and optimized model written to {}'''.format(SCREAMING_SNAKE_CASE_ ) )

              UpperCAmelCase  =						onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE_ )
              UpperCAmelCase  =						ort_sess.run(
                  SCREAMING_SNAKE_CASE_			,	{
                      '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
                      '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
                      '''num_beams''': np.array(SCREAMING_SNAKE_CASE_ ),
                      '''max_length''': np.array(SCREAMING_SNAKE_CASE_ ),
                      '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id ),
                  }			,	)

              np.testing.assert_allclose(summary_ids.cpu().numpy()			,	ort_out[0]			,	rtol=1e-3			,	atol=1e-3 )

              logger.info('''Model outputs from torch and ONNX Runtime are similar.''' )
              logger.info('''Success.''' )
def       __snake_case		( )							->     List[str]:

       """simple docstring"""


       UpperCAmelCase  =						parse_args()
       UpperCAmelCase  =						5
       UpperCAmelCase  =						4

       # Make one log on every process with the configuration for debugging.
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,	datefmt='''%m/%d/%Y %H:%M:%S'''			,	level=logging.INFO			,	)

       logger.setLevel(logging.INFO )
       transformers.utils.logging.set_verbosity_error()

       UpperCAmelCase  =						torch.device(args.device )

       UpperCAmelCase,					UpperCAmelCase  =						load_model_tokenizer(args.model_name_or_path			,	SCREAMING_SNAKE_CASE_ )

       if model.config.decoder_start_token_id is None:
              raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined''' )

       model.to(SCREAMING_SNAKE_CASE_ )

       if args.max_length:
              UpperCAmelCase  =						args.max_length

       if args.num_beams:
              UpperCAmelCase  =						args.num_beams

       if args.output_file_path:
              UpperCAmelCase  =						args.output_file_path
       else:
              UpperCAmelCase  =						'''BART.onnx'''

       logger.info('''Exporting model to ONNX''' )
       export_and_validate_model(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
    main()








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_     						=				{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


